Knowledge Graph Expert

Job Description

Role Overview
We are looking for a Knowledge Graph Expert to design, build, and scale enterprise-grade knowledge graphs and ontologies for a fortune 500 company. This role sits at the intersection of data engineering, semantic web technologies, and AI, enabling intelligent data integration, discovery, and reasoning across complex datasets.
You will translate business problems into semantic models and develop graph-based solutions that power search, analytics, and GenAI applications.

Key Responsibilities

1. Knowledge Graph & Ontology Development

• Design, develop, and maintain enterprise ontologies and knowledge graphs aligned with business needs
• Define entities, relationships, taxonomies, and semantic models for complex domains
• Build reusable ontology frameworks and semantic layers for data interoperability

2. Data Modeling & Integration

• Integrate structured and unstructured data into unified knowledge graphs
• Develop ETL/ELT pipelines, streaming pipelines, and data ingestion frameworks
• Perform entity resolution, data enrichment, and linking across disparate data sources

3. Semantic Web Technologies

• Implement solutions using RDF, OWL, SPARQL, SHACL, SKOS, and related standards
• Ensure compliance with semantic web and FAIR data principles
• Build and optimize triple stores (e.g., GraphDB, Virtuoso, Neptune, Neo4j)

4. Graph Engineering & Querying

• Develop and optimize graph queries and APIs for downstream applications
• Build scalable graph architectures and ensure performance tuning
• Enable reasoning, inference, and semantic search capabilities

5. AI/ML & NLP Integration

• Leverage NLP techniques for knowledge extraction from unstructured data
• Support GenAI use cases using knowledge graphs (RAG, grounding, explainability)
• Work with embeddings, graph ML, and link prediction models

6. Stakeholder Collaboration

• Translate business requirements into semantic models and graph solutions
• Work closely with data scientists, engineers, and domain experts
• Provide technical guidance and best practices for knowledge graph adoption

7. Governance & Quality

• Define data quality rules, validation frameworks, and governance standards
• Maintain metadata, taxonomy, and ontology lifecycle management
• Ensure scalability, consistency, and reusability of graph assets

Required Skills & Experience

• 5 - 10 years of experience.
• Technical Skills
• Strong experience in:
• Knowledge Graphs & Ontology Engineering
• RDF, OWL, SPARQL, SHACL
• Graph databases (GraphDB, Neo4j, Amazon Neptune, TigerGraph, etc.)
• Programming: Python (preferred), Java/Scala (optional)
• Data Engineering: ETL pipelines, APIs, distributed data systems
• Experience with semantic modeling, taxonomy design, and linked data
• Good to Have
• NLP frameworks (SpaCy, NLTK, Transformers)
• Graph ML / embeddings / GNNs
• Experience with GenAI / LLM integration
• Tools like Protg, TopBraid
• Key Competencies
• Strong problem-solving and analytical thinking
• Ability to bridge business and technical domains
• Excellent communication and stakeholder management
• Attention to data quality, governance, and scalability

More Info

Education